A signature of six-hypoxia-related genes to evaluate the tumor immune microenvironment and predict prognosis in gastric cancer.

Background: Hypoxia will trigger a series of immunosuppressive process in tumor microenvironment, leading to the progression in gastric cancer (GC). This research aims to establish a prognostic model made up of hypoxia-risk-related genes in GC.

Methods: Hypoxic genes were outlined via the protein-protein interaction network.

Potential prognostic biomarkers of sudden cardiac death discovered by machine learning.

Objective: Sudden cardiac death (SCD) is a serious public health burden. This study aims to find prognostic biomarkers of SCD using machine learning.

Methods: The myocardial samples from 21 accidental death and 82 sudden death donors were compared to seek for differential genes.

8DEstablishment and validation of a hypoxia-related signature predicting prognosis in hepatocellular carcinoma.

Background: Hypoxia plays a crucial role in immunotherapy of hepatocellular carcinoma (HCC) by changing the tumor microenvironment. Until now the association between hypoxia genes and prognosis of HCC remains obscure. We attempt to construct a hypoxia model to predict the prognosis in HCC.

A five-lncRNA model predicting overall survival in gastric cancer compared with normal tissues.

Aims: In cancer research, normal tissues adjacent to the tumor are usually defined as controls to compare with tumor samples, in order to screen out cancer-related genes. Although there is no obvious difference in pathology between normal tissues adjacent to the tumor and healthy tissues, there are significant changes at the molecular level. We aim to explore more potential tumor biomarkers using healthy tissues as controls rather than normal tissues adjacent to the tumor.

A genomic-clinicopathologic nomogram for predicting overall survival of hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a common digestive tumor with great heterogeneity and different overall survival (OS) time, causing stern problems for selecting optimal treatment. Here we aim to establish a nomogram to predict the OS in HCC patients.

Methods: International Cancer Genome Consortium (ICGC) database was searched for the target information in our study.